These are the revenue analytics responsibilities I would want a GTM leader to see in my background: forecasting, scenario planning, funnel leakage, executive reporting, annual planning support, trend analysis, data integration, and cross-functional cadence.
The useful question is not, "Do we have a forecast?" It is, "Do we have enough qualified pipeline, capacity, conversion, and timing to make the forecast believable?" That is where revenue modeling becomes an operating control instead of a spreadsheet exercise.
My fit is in the underlying pieces: funnel models, CAC and payback views, stage-velocity dashboards, product portfolio reporting, and executive scorecards. The next layer is using those inputs to support pipeline coverage and quota capacity conversations.
Pipeline coverage model, forecast bridge, quota capacity inputs, source-to-revenue confidence.
Strategic scenario planning: sensitivity models for pricing changes, product mix, discounting, and attach-rate scenarios.
The closest pattern from my work is the way I approach tradeoff analysis: isolate the driver, build a simple scenario layer, and show leadership what changes if volume, conversion, price, or mix moves. That pattern applies whether the lever is SaaS package mix, service attach rate, discounting, or paid-channel reallocation.
Funnel optimization: deep-dive conversion analysis across the customer journey.
Website Squirrel is the clearest example: budget was rising and CAC was getting worse. The fix came from working backward through attribution, stage conversion, high-intent behavior, and segment quality until the real leakage point was visible. Netgain added the longer-cycle version: stage velocity, multi-touch influence, and mid-funnel movement.
Executive reporting: clear performance narratives and executive recommendations for GTM leadership.
At Foxit, the executive problem was fragmented reporting across product, billing, support, and marketing. My role was to turn separate data sources into one decision-ready view. The value was not the dashboard by itself; it was the operating narrative: what changed, what mattered, and what the team should do next.
Annual planning: territory strategy, sales capacity planning, and annual GTM goal setting.
My strongest fit here is the planning layer underneath the work: connecting targets to funnel math, stage conversion, campaign capacity, and reporting cadence. I would treat annual planning as a model plus a governance rhythm: what target are we covering, what assumptions drive it, and where do we revisit the plan when reality moves?
Trend analysis: performance trends across sales stages, verticals, and product lines.
Across Foxit and Netgain, the trend question was rarely one metric. It was product line, stage, source, lifecycle motion, and customer segment moving together. The job was to show where performance was strong, where the trend was weakening, and whether the next move was budget, messaging, routing, nurture, or operational follow-up.
Data integration: aggregate Salesforce and internal systems into unified revenue views.
Foxit is the strongest proof point: product usage, billing, support, and marketing signals had to become one reconciled layer before leaders could trust the story. In other work, I have used SQL, Power Query, Power BI, Python, CRM data, and internal exports to build the same pattern: source systems stay where they belong, but the decision layer becomes shared.
Cross-functional collaboration: partner with Data and RevOps so reporting scales into company-wide frameworks.
The common thread in my work is cross-functional alignment: marketing with sales, product with support, billing with executive reporting, operations with vendor accountability. The technical artifact matters, but the operating agreement is what keeps it alive: definitions, owners, cadence, and escalation paths.
The specific output changes by team. A CRO may need pipeline coverage and stage velocity. A CFO may need CAC efficiency, payback, and forecast confidence. RevOps may need data definitions, routing logic, and owner clarity. The pattern is the same: make the revenue path visible enough that leadership can decide what to scale, fix, test, or watch.